{"id":3453,"date":"2011-04-04T19:50:59","date_gmt":"2011-04-04T19:50:59","guid":{"rendered":"http:\/\/hgpu.org\/?p=3453"},"modified":"2011-04-04T19:50:59","modified_gmt":"2011-04-04T19:50:59","slug":"migrating-real-time-depth-image-based-rendering-from-traditional-to-next-gen-gpgpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3453","title":{"rendered":"Migrating real-time depth image-based rendering from traditional to next-gen GPGPU"},"content":{"rendered":"<p>This paper focuses on the current revolution in using the GPU for general-purpose computations (GPGPU), and how to maximally exploit its powerful resources. Recently, the advent of next-generation GPGPU replaced the traditional way of exploiting the graphics hardware. We have migrated real-time depth image-based rendering &#8211; for use in contemporary 3DTV technology &#8211; and noticed however that using both GPGPU paradigms leads to a higher performance than non-hybrid implementations. Using this paper, we want to sensitize other researchers to reconsider before migrating their implementation completely, and use our practical migration rules to achieve maximum performance with minimal effort.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper focuses on the current revolution in using the GPU for general-purpose computations (GPGPU), and how to maximally exploit its powerful resources. Recently, the advent of next-generation GPGPU replaced the traditional way of exploiting the graphics hardware. We have migrated real-time depth image-based rendering &#8211; for use in contemporary 3DTV technology &#8211; and noticed [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,3],"tags":[1782,144],"class_list":["post-3453","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-rendering"],"views":1663,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3453","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3453"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3453\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3453"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3453"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3453"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}